In-hospital treatment and outcomes of heart failure in specialist and non-specialist services: a retrospective cohort study in the elderly.

BACKGROUND: Heart failure is common in the elderly and is associated with high rates of hospitalisation, readmission and mortality. International guidelines however are not frequently implemented in this population. METHODS: We retrospectively studied the clinical profile, investigations, treatment on discharge, length of hospital stay, readmission rate and mortality in 261 patients, aged ≥75 years, with a discharge diagnosis of heart failure. Clinical frailty was estimated using the Canadian Study of Health and Aging clinical frailty scale. RESULTS: Hypertension (64%), atrial fibrillation (50.6%) and ischaemic heart disease (46%) were common, and 75.6% of patients were clinically vulnerable or frail. 23.5% of admitters had an inpatient echocardiogram and 20% of patients had at least one readmission episode for heart failure. On discharge, 64.6% of admissions were treated with an ACE inhibitor or angiotensin II receptor antagonist, 49.3% with a ß blocker and 28.7% with an aldosterone receptor antagonist (ARA). Patients discharged from cardiology wards were more likely to receive a ß blocker (p<0.05) versus care of elderly (COE) wards and readmitters were more likely to receive an ARA (p<0.05) versus patients with a single admission. In total, 34 inpatient deaths were recorded (13%) and 80 deaths (30.7%) were recorded long-term (median follow-up 337 days). Long-term mortality was significantly lower in single admitters versus readmitters (p<0.0001) and in those managed on cardiology wards versus COE wards (p<0.05). CONCLUSIONS: Compared with patients hospitalised on geriatric wards, those admitted to cardiology units were discharged more frequently with recommended medications and had a lower long-term mortality.

Merging medical informatics and automated diagnostic methods.

In many instances disease diagnosis is more of an art than a science due to the complexity of disease, lack of detailed information on parameters that are indicative of the disease, and lack of sufficient data to apply these parameters to both diagnosis and treatment. Broad-based expansion of electronic health records (EHRs) will produce additional data for improved model development. However many obstacles remain. Patient record content is not broadly available because of privacy concerns and the lack of standardization of EHR formats. If available on a large scale, de-identified medical records can provide a basis for development of disease models by removing privacy concerns. Once comprehensive disease models have been developed that assist in identifying possible diseases and also include parameters that were utilized along with their relative importance, automated analytic methods can be used to indicate the likelihood of the presence of specific diseases. Although the physician will always remain as the final expert, these methods can provide an expanded information set and provide analysis that is too complex for standard methods.

Staff perceptions of respiratory rate measurement in a general hospital.

BACKGROUND: the National Institute for Health and Care Excellence (NICE) (2007) states that 'respiratory rate is the best marker of a sick patient and is the first observation that will indicate a problem or deterioration in condition'. It is therefore crucial that staff are confident that respiratory rates are recorded accurately. AIMS: to assess perceptions of clinical staff regarding methods of assessment and reliability of respiratory rate recordings in observation charts. METHODS: we developed a questionnaire using best practice guidelines. Some 41 ward-based clinical staff completed the questionnaires. FINDINGS: confidence in the reliability of recordings is very low. Clinical staff think recordings are often estimated with no formal measurement, with 'perceived lack of time' being the most commonly cited explanation for inappropriate assessment. CONCLUSIONS: essential clinical information is not being used, as clinical staff lack confidence that it has been assessed correctly. Furthermore, inaccurate recordings could be actively misleading clinical care.

Overcoming barriers to development of cooperative medical decision support models.

Attempts to automate the medical decision making process have been underway for the at least fifty years, beginning with data-based approaches that relied chiefly on statistically-based methods. Approaches expanded to include knowledge-based systems, both linear and non-linear neural networks, agent-based systems, and hybrid methods. While some of these models produced excellent results none have been used extensively in medical practice. In order to move these methods forward into practical use, a number of obstacles must be overcome, including validation of existing systems on large data sets, development of methods for including new knowledge as it becomes available, construction of a broad range of decision models, and development of non-intrusive methods that allow the physician to use these decision aids in conjunction with, not instead of, his or her own medical knowledge. None of these four requirements will come easily. A cooperative effort among researchers, including practicing MDs, is vital, particularly as more information on diseases and their contributing factors continues to expand resulting in more parameters than the human decision maker can process effectively. In this article some of the basic structures that are necessary to facilitate the use of an automated decision support system are discussed, along with potential methods for overcoming existing barriers.

Uncertainty and complexity in personal health records.

New technologies in medicine have led to an explosion in the number of parameters that must be considered when diagnosing and treating a patient. Because of this high volume of data it is not possible for the human decision maker to take all information into account in arriving at a decision. Automated methods are needed to effectively evaluate electronic information in many formats and provide summaries to the medical professional. The task is complicated by the complexity of the data and the potential uncertainty of some of the results. In this article complexity and uncertainty in medical data are discussed in terms of both representation and types of analysis. Methods that can address multiple complex data types are illustrated and examples are provided for specific medical problems. These methods are particularly important for automated trend analysis in the personal health record as small errors can be propagated through the complex system resulting in incorrect diagnosis and treatment.

Diagnostic models based on personalized analysis of trends (PAT).

Many changes have taken place in medicine over the last century. In the first-half of the 20th century physicians were faced with the challenge of making diagnoses with too little information, often resorting to exploratory surgery to confirm the presence or absence of a condition. Due to rapid technological advances during the second-half of the 20th century, and continuing to this day, the position of the physician has now shifted from an information-poor environment to an environment with too much information, often exceeding the limits of human decision-making capabilities. To take full advantage of all available information, a new approach based on refined automated decision support methods is needed to assist the physician in the decision-making process. Medical decision support systems need to evolve from stand-alone systems to cooperative systems in which the physician becomes the decision maker, but relies on the decision support system to sift through information to determine relevant trends. In this paper, a decision support system that combines a number of methodologies for trend analysis is described, along with examples in cardiology. The methods have also been used in applications in neurology as well as cancer diagnosis and prognosis.

Multidimensional medical decision making.

A century ago the physician had too little information on which to determine accurate diagnoses. Due to the rapid progression of technology in the Twentieth Century and the beginning of the Twenty-First Century, this situation changed significantly. Now the physician is faced with multi-parameter analyses that include sophisticated imaging, advanced cardiovascular studies, extensive laboratory tests, and genetic information, all of which impact diagnosis, treatment, and prognosis. New informatics tools are needed to assist, not replace, the physician in the decision process. Decision analysis tools must be flexible to accommodate new methods of diagnosis as well as advances in information technology. In this article, basic structures are defined that can form the basis of such a system.

Temporal trend analysis in personal health records.

The creation of a universal standard for electronic medical records (EMR) remains a work in progress. The existence of a standard EMR that can be accessed by medical professionals anywhere in the world as well as by the patient will have a significant impact on the way that medicine is practiced. In addition to having information readily available, it would present the possibility to do individual trend analysis on each patient based on information accumulated over a lifetime. This information would permit diagnosis based on the individual rather than solely on population statistics. A comprehensive system for performing trend analysis on the multitude of data types relevant to medical decision making requires the creation and integration of a number of methods. A general approach based on a hybrid combination of automated decision support methods is described and illustrated for using trend data applied to cardiac diagnosis. The methods are general and can be applied to any application area.

Combining evidence in hybrid medical decision support models.

Hybrid methods are particularly useful for building diagnostic models based on biomedical data due to the wide variety of data types that are routinely encountered. Evaluation of the effectiveness of hybrid systems is complicated when multiple methods are combined to reach a conclusion. In the work described here, methods for combining results based on the general reliability of each model as well as its applicability to the case under evaluation are presented. Reliability measures differ depending on whether symbolic or numeric information is analyzed and depend on the strength of the decision algorithm as well as the soundness of the domain knowledge upon which the decision is based. In addition to reliability, combination of results is complicated by the need to weight each method to form the final conclusion. Weighting factors depend on the degree of certainty that the decision is correct for each of the methods. The process is illustrated in an application to cardiac diagnosis.

Synchronization measures of the scalp electroencephalogram can discriminate healthy from Alzheimer's subjects.

Three synchronization measures are applied to scalp electroencephalogram (EEG) data collected from 20 patients diagnosed to have either: (1) no dementia, (2) mild cognitive impairment (MCI), or (3) Alzheimer's disease (AD). We apply the three synchronization measures--the phase synchronization, and two measures of nonlinear interdependency--to the data collected from awake patients resting with eyes closed. We show that the synchronization in potential between electrodes near the left and right occipital lobes provides a statistically significant discriminant between the healthy and AD subjects, and the MCI and AD subjects. None of the three measures appears able to distinguish between the healthy and MCI subjects, although MCI subjects show synchronization values intermediate between healthy subjects (with high synchronization values) and AD subjects (with low synchronization values) on average.

Comparison of cardiovascular risk of noncardiac surgery following coronary angioplasty with versus without stenting.

Previous studies have shown a high incidence of cardiovascular complications when noncardiac surgery (NCS) is performed after coronary stenting. No study has compared the outcomes of NCS after stenting compared with percutaneous transluminal coronary angioplasty (PTCA) alone. The records of all patients who underwent NCS within 3 months of percutaneous coronary intervention at our institution were reviewed for adverse clinical events with the end points of acute myocardial infarction, major bleeding, and death < or = 6 months after NCS. A total of 216 consecutive patients were included in the study. Of these, 122 (56%) underwent PTCA and 94 (44%) underwent stenting. A total of 26 patients (12%) died, 13 in the stent group (14%) and 13 in the PTCA group (11%), a nonsignificant difference. The incidence of acute myocardial infarction and major bleeding was 7% and 16% in the stent group and 6% and 13% in the PTCA group (p = NS), respectively. Significantly more events occurred in the 2 groups when NCS was performed within 2 weeks of percutaneous coronary intervention. In conclusion, our study has demonstrated high rates of perioperative morbidity and mortality after NCS in patients undergoing PTCA alone, as well as stenting. These findings support the current guidelines regarding the risk of NCS after stenting but suggest they be extended to PTCA as well.

Neural network models for biosignal analysis.

Signal analysis provides important clues for diagnosis of disease in many arenas, particularly in cardiology. While each result may give good diagnostic information, a comprehensive decision model requires the combination of results. In the work described here, a neural network model is used to combine various features obtained through signal analysis. The original model is based on electrocardiogram (ECG analysis). This model is extended in two ways: the neural network model is expanded to include other clinical parameters in addition to the ECG and the method is generalized to include other biosignals.

Intelligent analysis of biosignals.

While biosignal analysis has been a mainstay in many medical applications for a number of decades, notably in cardiology, problems remain in the analysis of high volume data and in comparison among time series. Effective comparisons can determine if the state of a particular patient has changed significantly. These comparisons are useful in both long-term and short-term monitoring. Methods will vary depending on the type of signal and the situation. Intelligent agents permit the activation of the appropriate analysis for the signal and the current situation. Agents are defined in terms of both methodology and function. The agent system is described and illustrated in applications to cardiology and neurology.

Diagnostic potential of nonlinear analysis of biosignals.

Biosignals have played an important role in medical diagnosis. The first biosignal to be used extensively was the electrocardiogram whose interpretation initially relied on manual analysis of paper tracings. Interpretation was based on variations of the normal QRS pattern associated with each heartbeat. Automated arrhythmia analysis was developed commercially and has been in standard clinical use for some time. The advent of Holter monitoring presented new challenges for the analysis of very long time series. New methods have been developed for this purpose, including nonlinear dynamical approaches. These methods have yielded important diagnostic clues. In this article, the diagnostic use of parameters derived from nonlinear analysis, both alone and in conjunction with other clinical information, is discussed.

Medical therapy for the overactive bladder in the elderly.

The overactive bladder is the commonest underlying bladder disorder causing urinary incontinence in elderly people. Management of the condition relies upon a holistic assessment of the problem, lifestyle adjustment, behavioural management and drug therapy. The majority of currently available drugs rely on their anti-muscarinic properties for efficacy. Both behavioural techniques and drug therapy are effective in treatment of the elderly and each modality has a particular role to play in successful treatment and maintenance of this condition.